Abstract

This work is part of the Perceptually Optimised Sound Zone project (posz.org) which
aims to develop sound zoning systems which reproduce audio programmes to multiple
listening zones within automotive and domestic environments. This work describes the
construction of a model to evaluate sound zoning systems.
A framework for evaluating auditory interference scenarios is described in which either
the target or interferer programme is masked, or where both programmes are audible
and the listening scenario has some degree of acceptability. Masking and acceptability
experiments were conducted to investigate the relationship between the two, and to
determine boundaries of audibility. A linear correlation was found between masking
and acceptability, and a linear regression model was constructed to predict thresholds
of acceptability from masking thresholds. A masking threshold model was adapted and
predictions were within 3 dB of the reported mean masking thresholds. Predictions of
acceptability, using a linear regression and masking model combination, accounted for
three quarters of the variance in acceptability.
Further work focused on speech target programmes based on listener comments that
the presence of speech a?ected acceptability. An experiment was conducted to gather
intelligibility and acceptability data. Results showed that a high speech intelligibility
marked the lower boundary of acceptability.
Existing models for intelligibility
prediction were evaluated and a time-windowed speech intelligibility index was shown
to predict intelligibility with RMSE = 10.8%.
Subsequently, a model was constructed to predict acceptability within these boundaries.
Two experiments were conducted gathering training and validation data, and a training
and selection procedure was carried out to methodically identify the most useful
features. The selected model predictions had acceptability scores of RMSE = 11.1?
17.9% across training and validation data.
Finally, an algorithm was proposed for the prediction of acceptability in auditory
interference scenarios. The algorithm consists of ?rst predicting masking thresholds to
determine the boundaries of acceptability. Then, for non-speech target programmes, the
acceptability is predicted using a linear regression to the masking threshold; for speech
target programmes, the intelligibility is calculated to revise the lower acceptability
boundary and the speech acceptability model is used to predict acceptability.